LIGHTWEIGHT MULTI-VIEW-GROUP NEURAL NETWORK FOR 3D SHAPE CLASSIFICATION

In this work, we propose LiteMVGNet, a novel lightweight neural network for 3D shape classification. It is based on depth maps generated by multi-view rendering of the corresponding 3D model. LiteMVGNet is designed to be lightweight and effective in various aspects. First, the views and corresponding depth maps are partitioned into groups. Next, depth map features for each group are separately extracted by an adapted MobileNetV2 block. Finally, the extracted group features are fused by an adapted MobileViT block. The views are partitioned by good geometrical semantics and ECAnet is utilized to facilitate extraction of effective features. As demonstrated by experiments, in comparison with the state-of-the-art benchmark models, the proposed one cuts the network parameter count by a third and more and reduces the floating-point operation count by even one or two orders of magnitude. Still, the proposed model yields classification accuracies comparable with the benchmark models.